Postdoc researcher in HiLIFE Invertebrate Zoology, Bioinformatics/Data Science

Helsinki, Finland
Joined September 2020
Maksim Nesterenko retweeted
Pytrf: a python package for finding tandem repeats from genomic sequences bmcbioinformatics.biomedcent…
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Maksim Nesterenko retweeted
Looks like a very useful R package for the analysis of morphological evolution on phylogenies: "We introduce MorphSim, an R package for simulating discrete morphological characters specifically designed for total- evidence and tip- dated phylogenetic analyses..."
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Maksim Nesterenko retweeted
Deep sleep may be one of the brain’s most powerful anti-anxiety tools. Researchers identified a sleep-active circuit linking the parafacial zone, parabrachial nucleus, and BNST that suppresses stress-related signaling during slow-wave sleep and prevents anxiety-like behavior. #SleepScience #Neuroscience #MentalHealth sciencedirect.com/science/ar…
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Maksim Nesterenko retweeted
scDIG: An R Shiny Application for Interactive Density-Based Gating of Single-Cell Proteomic and Transcriptomic Data biorxiv.org/content/10.64898…
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Maksim Nesterenko retweeted
PRIME: Atlas-Level Single-Cell and Spatial Transcriptomics Data Integration biorxiv.org/content/10.64898…
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Maksim Nesterenko retweeted
Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions biorxiv.org/content/10.64898…
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Maksim Nesterenko retweeted
то насколько все пизданулись не передать словами
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Maksim Nesterenko retweeted
Fabian Theis
@fabian_theisMay 12
Excited to share our RegVelo paper in Cell cell.com/cell/fulltext/S0092… We unify RNA velocity + GRNs into one model → better OOD prediction of perturbations (e.g. gene KOs), with examples incl. neural crest KO predictions 🔬 Big thanks to W Wang, Z Hu & T Sauka-Spengler 🙏
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Юмор в контексте современной России — практически единственное, что меня в этой современной России поддерживает.💀❤️
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Maksim Nesterenko retweeted
1/15 Want to decode pathway activity in single cells? Understand ssGSEA, GSVA, AUCell, and UCell—tools that score gene sets per cell. Here's how they work 🧵
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Maksim Nesterenko retweeted
PISA: a versatile interpretation tool for visualizing cis-regulatory rules in genomic data biorxiv.org/content/10.1101/…
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Maksim Nesterenko retweeted
Correlation Between Information Entropy and Functions of Gene Sequences in the Evolutionary Context: A New Way to Construct Gene Regulatory Networks from Sequence biorxiv.org/content/10.64898…
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Maksim Nesterenko retweeted
Nested-WGCNA: Nested co-expression network analysis identifies compact gene clusters in a black box academic.oup.com/bioinformat…
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Maksim Nesterenko retweeted
scMagnifier: resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering biorxiv.org/content/10.64898…
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Maksim Nesterenko retweeted
Sceodesic: Navigating the manifold of single-cell gene coexpression to discover interpretable gene programs biorxiv.org/content/10.1101/…
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Maksim Nesterenko retweeted
This paper took us 13 years and is one of the longest papers ever in Cell. Check it out & judge for yourself cell.com/cell/fulltext/S0092…
Replying to @JoinLifespan
Just because epigenetic drift follows a predictable pattern doesn't necessarily mean it's causal in aging. Predictable patterns can form from stochastic reactions. Also you cannot fix all extracellular damages with cellular rejuvenation. More here: olafurpall.substack.com/p/wh…
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Maksim Nesterenko retweeted
Have you heard of “zombie cells”? @Nature just published a commentary that will make you rethink what "life" means. Scientists from JCVI took bacterial cells, killed their genomes with a chemotherapy drug (mitomycin C crosslinks the DNA — permanently disabled), then transplanted a synthetic genome from a completely different species into these dead cell bodies. Some rebooted. Grew. Divided. Alive again — but running entirely on foreign DNA. The cell body is hardware. The genome is software. They wiped the drive, installed a new OS, and the machine booted. What makes this technically distinct from prior JCVI work: previous genome transplants competed with the host's native genome, requiring antibiotic selection to find successful transfers. Here, the host genome is already dead — only cells revived by the new genome survive. No selection markers, no competition. Cleaner, more scalable. The historical arc is worth appreciating: • 2007: genome transplant between species • 2010: first synthetic self-replicating cell • 2016: minimal genome (473 genes — the floor of life) • 2026: dead chassis + synthetic genome = living cell The question this raises isn't just technical. A M. capricolum body running a M. mycoides genome — what species is it? The answer is neither, which means species identity may live in the genome, not the cell. That has implications far beyond bacteria. Paper link: nature.com/articles/d41586-0…
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Maksim Nesterenko retweeted
Cell-type-specific transposable element demethylation and TAD remodeling in the aging mouse brain biorxiv.org/content/10.1101/…
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